EFFECT OF SOLVENT TYPE AND DRAINAGE HEIGHT ON ASPHALTENE PRECIPITATION FOR THE SOLVENT PERCOLATING GRAVITY DRAINAGE MECHANISM IN THE VAPOR EXTRACTION PROCESS
Bibliographic record
Abstract
The problems associated with highly viscous heavy oil reservoirs, excessive heat loss to the surrounding formations, low permeability carbonate reservoirs, and the large amount of CO2 emitted during thermal processes have made solvent-based heavy oil recovery methods more attractive than thermal methods. In this study, an extensive experimental investigation was carried out to evaluate the effect of solvent type and drainage height, as the key parameters in vapor extraction, on asphaltene precipitation. Two large visual rectangular sand-packed physical models with heights of 24 and 47 cm were employed to conduct the experimental studies. Propane, methane, and a propane/CO2 mixture were considered as the respective solvents in the experiments. Also, separate experiments were carried out to measure the asphaltene precipitation at different locations in the models. The results show that for almost all of the different solvents used in this study more asphaltene precipitation was observed close to the injection points and at the oil/solvent interface. Comparing the textures of the asphaltene precipitants from different locations in the models, it was found that the precipitants close to the injection points were more brittle, while the precipitants close to the production points were more ductile. After comparing the asphaltene precipitation in the small and large models when various solvents were used, it was observed that in the case of propane injection more asphaltene precipitation was observed at different locations in the physical models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".